Fragments, bookmarks, and things worth remembering

One million tokens of context at standard pricing. No premium tier, no special request. The practical difference is not the number. It is the absence of the wall. At 200K, you hit compaction mid-feature and lose the thread. The conversation forgets what it was building. You spend ten minutes re-explaining what you just spent an hour constructing. At 1M, each feature lives in a single, self-contained session. The context stays clean. The work stays coherent. You stop fighting the tool and start using it. That is the actual product improvement. Not the headline number. The fact that you never have to context-switch because the model did.

NVIDIA announced NemoClaw at GTC. A software stack that wraps OpenClaw with Nemotron models, OpenShell secure runtime, and guardrails. One command install. After OpenAI acquired OpenClaw in February, enterprises needed an independent, secure agent platform. NVIDIA filled the gap. Runs on everything from RTX PCs to DGX Spark.

Knuth learned that an open problem he had been working on for weeks was solved by Claude Opus 4.6 in about an hour. Filip Stappers posed the problem and coached Claude through 31 explorations with one rule: document progress in plan.md after every run. The agent reformulated, hit dead ends, backtracked, reframed, and converged on a construction for all odd m. Same structured-exploration loop as Karpathy’s autoresearch. Different domain, same meta-pattern.

The postscripts are wild. GPT-5.4 Pro produced a 14-page proof for even m. Multi-agent workflows (GPT + Claude) found simpler constructions. The Lean community formalized the proof within days.

Jeff Dean’s classic reference on computer operation latencies. L1 cache at 0.5ns, main memory at 100ns, SSD read at 16us, disk seek at 10ms, cross-continent roundtrip at 150ms. Knowing these magnitudes is the difference between guessing at bottlenecks and actually understanding where your time goes.